1. Field of the Invention
The present invention relates to a communication system, in particular to a data-dependent superimposed training system and a method thereof that use a precoding matrix to overcome the problem of identifying data.
2. Description of Related Art
In the field of communication systems, channel estimation is a very important part of the receiver, and pilot signals are equidistantly inserted into transmission data to obtain a mean square error of the smallest channel. In a conventional time division multiplexing (TDM) method, data and pilot signals are transmitted at different time points, but this method will lower the efficiency of bandwidths. Another method is to superimpose a training sequence with data before transmitting the data, but this method gives poor channel estimation. The data-dependent superimposed training system (DDST) not only maintains the efficiency of bandwidths, but also maintains the channel response of the channel estimation to a precision same as the time division multiplexing (TDM) method. Since the cyclic mean of the data must be removed before transmitting the data, the receiver cannot restore the signals effectively without knowing the existence of the cyclic mean. If a high-level modulation technology is used, the receiver will have a higher chance of producing a misjudgment.
At present, technologies capable of reducing data misjudgments in a data-dependent superimposed training system are disclosed. As disclosed by T. Whitworth, M. Ghogho, and D. C. McLernon in “Data Identifiability for Data-Dependent Superimposed Training,” in Proc. IEEE International Conference on Communications, Glasgow, UK, June 2007, pp. 2545-2550 and M. Ghogho, T. Whitworth, A. Swami, and D. C. McLernon in “Full-Rank and Rank-Deficient Precoding Schemes for Single-Carrier Block Transmissions,” was published in IEEE Trans Signal Process, vol. 57, no. 11, pp. 4433-4442, November 2009.
Although the aforementioned methods can find the lost values in a time domain and a frequency domain to reduce the probability of having data misjudgments, yet these methods cannot solve the issue of data misjudgment completely, and the computational complexity of the receiver is still very high. Therefore, it is a main subject of the present invention to provide a communication system and a method thereof capable of overcome the data misjudgment problem of the data-dependent superimposed training system thoroughly.